import numpy as np
import cv2 as cv
import os, time
from matplotlib import pyplot as plt
import pyautogui as pyautogui


# MIN_MATCH_COUNT = 10
MIN_MATCH_COUNT = 4

# img1 = cv.imread('./feature/test1.png')          # queryImage
# img2 = cv.imread('./testp/messi5.jpg') # trainImage
img1 = cv.imread('./test00.png')          # queryImage
img2_1 = pyautogui.screenshot() # trainImage
img2 = cv.cvtColor(np.asarray(img2_1),cv.COLOR_RGB2BGR)   # queryImage
# print(img1.shape)
# print(img2.shape)
# Initiate SIFT detector
sift = cv.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(img1,None)
kp2, des2 = sift.detectAndCompute(img2,None)
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)
flann = cv.FlannBasedMatcher(index_params, search_params)
matches = flann.knnMatch(des1,des2,k=2)
# store all the good matches as per Lowe's ratio test.
good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)
if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)
    M, mask = cv.findHomography(src_pts, dst_pts, cv.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()
    # print(img1.shape)
    h,w,d = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    # print(M)
    print('=====================================================================')
    # print(pts)
    dst = cv.perspectiveTransform(pts,M)
    print(dst)
    # print((dst[0][0] + dst[3][0])/2 + ',' + (dst[0][1] + dst[3][1])/2)
    img2 = cv.polylines(img2,[np.int32(dst)],True,255,3, cv.LINE_AA)
else:
    print( "Not enough matches are found - {}/{}".format(len(good), MIN_MATCH_COUNT) )
    matchesMask = None
draw_params = dict(matchColor = (0,255,0), # draw matches in green color
                   singlePointColor = None,
                   matchesMask = matchesMask, # draw only inliers
                   flags = 2)
img3 = cv.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.imshow(img3, 'gray'),plt.show()